In the last 10 years social media has effectively changed the way people communicate online. Millions of blogs, or services like Twitter and Facebook enabled new ways of expression for people worldwide. Billions of messages, blog posts, pictures, videos etc are published on a daily basis. As such, it has become very important for companies to find a way to analyze this real-time stream of information.
Many companies use a social monitoring service like uberVU to collect all mentions of a specific brand, a product, or an event. The way this usually works is by creating a persistent search, which will constantly collect and analyze all the new mentions of a specific brand, a product, or an event. However, for most of these searches there is a huge quantity of data that needs to be analyzed, making it very difficult for a company to identify the people that they should be interacting with, and to analyze how the messages virally spread in the social space.
There are providers of influence metrics, which offer systems that analyze all the data streams from one or more identified users, and compute a general influence rank. These systems may be capable of offering additional deeper analysis; such as identifying the main themes of influence for the users they rank.
The problem with this approach is that it is very difficult to use the same influence metric for all data streams. For example, maybe a user is very popular and influent when the user talks about cars, but the user's influence related to a certain food ingredient is close to 0. Moreover, the existing influence metrics require a prior identification of an influent user, and measure extent of this influence only after such identification.
As such, there is a need for a system that is capable of analyzing large data sets from social monitoring to detect the most influent social media.